Adaptive Assessment of Power System Transient Stability Based on Active Transfer Learning

doi:10.11930/j.issn.1004-9649.202404033

Abstract

Abstract:

This paper constructs a framework based on active transfer learning. The basic model is built and trained based on the original scene data. The update mechanism is started when the performance of the model decreases due to the change of the running scene. A large number of samples without stable state are generated by short-term time-domain simulation, and a small batch of labeled samples are generated by complete simulation. The active learning method based on variational adversarial is used to learn the potential feature representation space of the data, and the unlabeled samples with the largest amount of information are selected and labeled according to the confidence. The basic model parameters are migrated and fine-tuned with labeled samples to save the update time while ensuring the migration accuracy. The IEEE 39 node verifies the effectiveness of the proposed method.

Key words: power system, transient stability assessment, transfer learning, active learning

CLC Number:

TM732

Chenhao ZHAO, Zaibin JIAO, Chenghao LI, Di ZHANG, Penghui ZHANG. Adaptive Assessment of Power System Transient Stability Based on Active Transfer Learning[J]. Electric Power, 2025, 58(1): 70-77.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202404033

https://www.electricpower.com.cn/EN/Y2025/V58/I1/70

Figures/Tables 12

References 24

1	娄奇鹤, 李荣盛, 谭捷, 等. 基于卷积神经网络的暂稳极限功率计算[J]. 中国电力, 2024, 57 (4): 211- 219.
	LOU Qihe, LI Rongsheng, TAN Jie, et al. Calculation of transient stability limit based on convolutional neural network[J]. Electric Power, 2024, 57 (4): 211- 219.
2	周生存, 罗毅, 易煊承, 等. 考虑数据缺失的图注意力网络暂态稳定评估[J]. 中国电力, 2024, 57 (5): 157- 167.
	ZHOU Shengcun, LUO Yi, YI Xuancheng, et al. Transient stability assessment of graph attention networks considering data missing[J]. Electric Power, 2024, 57 (5): 157- 167.
3	向川, 陈鎏凯, 陈勇, 等. 基于深层级联残差图卷积的暂态稳定评估模型及其实际电网应用[J]. 广东电力, 2024, 37 (6): 62- 69.
	XIANG Chuan, CHEN Liukai, CHEN Yong, et al. Application of transient stability assessment model based on deep cascading residual graph convolution network in real world power grids[J]. Guangdong Electric Power, 2024, 37 (6): 62- 69.
4	陈水耀, 胡晨, 马伟, 等. 基于故障信息的风机并网系统暂态稳定分析方法[J]. 浙江电力, 2023, 42 (9): 89- 98.
	CHEN Shuiyao, HU Chen, MA Wei, et al. A Transient stability analysis method for power systems with wind turbines integrated based on fault information[J]. Zhejiang Electric Power, 2023, 42 (9): 89- 98.
5	于枋彤, 刘铖, 张宇驰. 输电线开断控制对交直流混联电力系统暂态稳定性影响研究[J]. 东北电力大学学报, 2023, 43 (1): 85- 91.
	YU Fangtong, LIU Cheng, ZHANG Yuchi. Research on influence of transmission line breaking control on transient stability of AC/DC hybrid power system[J]. Journal of Northeast Electric Power University, 2023, 43 (1): 85- 91.
6	李宇骏, 陆艺源, 牟同鹏, 等. 新能源发电经换流器并网系统的稳定性分析与控制综述[J]. 电网与清洁能源, 2023, 39 (12): 79- 94.
	LI Yujun, LU Yiyuan, MU Tongpeng, et al. A review of stability analysis and control of renewable energy integration systems via converters[J]. Power System and Clean Energy, 2023, 39 (12): 79- 94.
7	曾垂辉, 谢俊, 李智, 等. 探究无功控制回路对构网型并网逆变器暂态稳定性的影响[J]. 东北电力大学学报, 2024, 44 (4): 38- 45.
	ZENG Chuihui, XIE Jun, LI Zhi, et al. Investigating the impact of reactive power control loops on the transient stability of grid-forming inverters[J]. Journal of Northeast Electric Power University, 2024, 44 (4): 38- 45.
8	伏绍鑫, 张路, 唐翰峰, 等. 考虑柔性电热负荷的区域综合能源系统低碳经济调度[J]. 电力科技与环保, 2023, 39 (5): 417- 428.
	FU Shaoxin, ZHANG Lu, TANG Hanfeng, et al. Low-carbon economic dispatch of community integrated energy system considering flexible electric heating load[J]. Electric Power Technology and Environmental Protection, 2023, 39 (5): 417- 428.
9	刘昭睿, 王凯, 王新建, 等. 提升末端区域电网供电能力的调控方案及稳控策略研究[J]. 内蒙古电力技术, 2024, 42 (3): 52- 60.
	LIU Zhaorui, WANG Kai, WANG Xinjian, et al. Research on regulation scheme and stability control strategy for enhancing power supply capacity of end-region power grid[J]. Inner Mongolia Electric Power, 2024, 42 (3): 52- 60.
10	王中行, 周元贵, 张学广. 基于人工智能算法的风电机组状态监测和故障诊断技术研究综述[J]. 东北电力大学学报, 2024, 44 (1): 42- 51.
	WANG Zhongxing, ZHOU Yuangui, ZHANG Xueguang. Review of artificial intelligence algorithms-based wind turbine condition monitoring and fault diagnosis techniques[J]. Journal of Northeast Electric Power University, 2024, 44 (1): 42- 51.
11	李子凯, 岳宝强, 杨波, 等. 适于低功率状态的多特征融合负荷分解方法[J]. 山东电力技术, 2024, 51 (1): 68- 76.
	LI Zikai, YUE Baoqiang, YANG Bo, et al. Multi-feature fusion load disaggregation method for low-power states[J]. Shandong Electric Power, 2024, 51 (1): 68- 76.
12	姜力杨, 盖晨昊, 齐航, 等. 数据驱动的电力系统动态安全评估研究综述[J]. 山东电力技术, 2024, 51 (4): 27- 35.
	JIANG Liyang, GAI Chenhao, QI Hang, et al. Review on data-driven dynamic security assessment of power systems[J]. Shandong Electric Power, 2024, 51 (4): 27- 35.
13	邢法财, 麻常辉, 苗伟威, 等. 考虑自然灾害影响的节点恢复重要度评估与目标网架重构[J]. 浙江电力, 2024, 43 (10): 53- 64.
	XING Facai, MA Changhui, MIAO Weiwei, et al. Assessment of the importance of node recovery and grid framework reconfiguration with consideration of natural disaster impacts[J]. Zhejiang Electric Power, 2024, 43 (10): 53- 64.
14	武永强, 郭凯, 王宇强, 等. 构网型储能系统并网测试要求及测试实例分析[J]. 内蒙古电力技术, 2024, 42 (2): 8- 16.
	WU Yongqiang, GUO Kai, WANG Yuqiang, et al. Testing requirements and case analysis of grid-forming energy storage system[J]. Inner Mongolia Electric Power, 2024, 42 (2): 8- 16.
15	王汝田, 王世琪. 分裂源双级矩阵变换器及其调制策略的研究[J]. 东北电力大学学报, 2023, 43 (3): 47- 54.
	WANG Rutian, WANG Shiqi. Research on split-source two-stage matrix converter and its modulation strategy[J]. Journal of Northeast Electric Power University, 2023, 43 (3): 47- 54.
16	李博, 石红晖, 马强, 等. 风电-火电-压缩空气储能综合能源系统运行特性研究[J]. 电力科技与环保, 2024, 40 (2): 168- 177.
	LI Bo, SHI Honghui, MA Qiang, et al. The operating characteristics research of integrated energy system based on the wind, coal-fired power plant and compressed air energy storage[J]. Electric Power Technology and Environmental Protection, 2024, 40 (2): 168- 177.
17	孙黎霞, 白景涛, 周照宇, 等. 基于双向长短期记忆网络的电力系统暂态稳定评估[J]. 电力系统自动化, 2020, 44 (13): 64- 72.
	SUN Lixia, BAI Jingtao, ZHOU Zhaoyu, et al. Transient stability assessment of power system based on bi-directional long-short-term memory network[J]. Automation of Electric Power Systems, 2020, 44 (13): 64- 72.
18	贺日星, 杨宝峰, 陈瀚栋, 等. 抑制直流输电换相失败方法研究[J]. 内蒙古电力技术, 2023, 41 (6): 75- 83.
	HE Rixing, YANG Baofeng, CHEN Handong, et al. Research of methods for suppressing commutation failure in HVDC transmission[J]. Inner Mongolia Electric Power, 2023, 41 (6): 75- 83.
19	陈哲, 李山林, 林达, 等. 考虑拓扑切换暂态波动的微电网群分布式协同控制策略[J]. 浙江电力, 2023, 42 (11): 1- 10.
	CHEN Zhe, LI Shanlin, LIN Da, et al. A distributed cooperative control strategy for MGC considering transient fluctuations in topology switching[J]. Zhejiang Electric Power, 2023, 42 (11): 1- 10.
20	张若愚, 吴俊勇, 李宝琴, 等. 基于迁移学习的电力系统暂态稳定自适应预测[J]. 电网技术, 2020, 44 (6): 2196- 2205.
	ZHANG Ruoyu, WU Junyong, LI Baoqin, et al. Self-adaptive power system transient stability prediction based on transfer learning[J]. Power System Technology, 2020, 44 (6): 2196- 2205.
21	XIE J, SUN W. A transfer and deep learning-based method for online frequency stability assessment and control[J]. IEEE Access, 2021, 9, 75712- 75721. DOI
22	汤奕, 崔晗, 党杰. 基于继承思想的时变性电力系统暂态稳定预测[J]. 中国电机工程学报, 2021, 41 (15): 5107- 5119.
	TANG Yi, CUI Han, DANG Jie. Transient stability prediction of time-varying power systems based on inheritance[J]. Proceedings of the CSEE, 2021, 41 (15): 5107- 5119.
23	焦昊, 殷岩岩, 吴晨, 等. 基于安全强化学习的主动配电网有功-无功协调优化调度[J]. 中国电力, 2024, 57 (3): 43- 50.
	JIAO Hao, YIN Yanyan, WU Chen, et al. Coordinated optimization of active and reactive power of active distribution network based on safety reinforcement learning[J]. Electric Power, 2024, 57 (3): 43- 50.
24	YAN R, WANG Z Y, YUAN Y X, et al. Information entropy based prioritization strategy for data-driven transient stability batch assessment[J]. CSEE Journal of Power and Energy Systems, 2021, 7 (3): 443- 455.

真实标签	预测标签
真实标签	稳定	失稳
稳定	${T_{\text{P}}}$	${F_{\text{N}}}$
失稳	${F_{\text{P}}}$	${T_{\text{N}}}$

真实标签	预测标签
真实标签	稳定	失稳
稳定	${T_{\text{P}}}$	${F_{\text{N}}}$
失稳	${F_{\text{P}}}$	${T_{\text{N}}}$

模型	准确率	召回率	精确率	F1分数
LR	93.23	94.52	94.67	94.59
SVM	95.82	97.02	96.31	96.67
XGBoost	96.99	98.01	97.20	97.61
DNN	96.96	96.81	98.38	97.59
CNN	96.41	97.57	96.65	97.11
TCN	97.12	97.85	97.55	97.70
GRU	96.67	97.09	97.59	97.34
TCN-GRU	98.32	99.28	98.67	98.98

模型	准确率	召回率	精确率	F1分数
LR	93.23	94.52	94.67	94.59
SVM	95.82	97.02	96.31	96.67
XGBoost	96.99	98.01	97.20	97.61
DNN	96.96	96.81	98.38	97.59
CNN	96.41	97.57	96.65	97.11
TCN	97.12	97.85	97.55	97.70
GRU	96.67	97.09	97.59	97.34
TCN-GRU	98.32	99.28	98.67	98.98

目标系统	准确率	召回率	精确率	F1分数
1	82.51	85.95	93.21	89.43
2	76.11	81.65	89.22	85.27
3	80.30	85.07	91.27	88.06